Computation Rate Maximization for Multiuser Mobile Edge Computing Systems With Dynamic Energy Arrivals

This paper considers an energy harvesting (EH) based multiuser mobile edge computing (MEC) system, where each user utilizes the harvested energy from renewable energy sources to execute its computation tasks via computation of-floading and local computing. Towards maximizing the system weighted comp...

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Vydáno v:2021 IEEE/CIC International Conference on Communications in China (ICCC) s. 312 - 317
Hlavní autoři: Lin, Zhifei, Wang, Feng, Liu, Licheng
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 28.07.2021
Témata:
Benchmark testing
computation offloading
Convex functions
convex optimization
Energy harvesting
energy harvesting (EH)
Mobile edge computing (MEC)
Performance gain
Renewable energy sources
Resource management
Task analysis
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Abstract This paper considers an energy harvesting (EH) based multiuser mobile edge computing (MEC) system, where each user utilizes the harvested energy from renewable energy sources to execute its computation tasks via computation of-floading and local computing. Towards maximizing the system weighted computation rate (i.e., the number of weighted users' computing bits within a finite time horizon) subject to the users' energy causality constraints due to dynamic energy arrivals, the decision for joint computation offloading and local computing over time is optimized over time. Assuming that the profile of channel state information and dynamic task arrivals at the users is known in advance, the weighted computation rate maximization problem becomes a convex optimization problem. Building on the Lagrange duality method, the well-structured optimal solution is analytically obtained. Both the users' local computing and offloading rates are shown to have a monotonically increasing structure. Numerical results show that the proposed design scheme can achieve a significant performance gain over the alternative benchmark schemes.
AbstractList This paper considers an energy harvesting (EH) based multiuser mobile edge computing (MEC) system, where each user utilizes the harvested energy from renewable energy sources to execute its computation tasks via computation of-floading and local computing. Towards maximizing the system weighted computation rate (i.e., the number of weighted users' computing bits within a finite time horizon) subject to the users' energy causality constraints due to dynamic energy arrivals, the decision for joint computation offloading and local computing over time is optimized over time. Assuming that the profile of channel state information and dynamic task arrivals at the users is known in advance, the weighted computation rate maximization problem becomes a convex optimization problem. Building on the Lagrange duality method, the well-structured optimal solution is analytically obtained. Both the users' local computing and offloading rates are shown to have a monotonically increasing structure. Numerical results show that the proposed design scheme can achieve a significant performance gain over the alternative benchmark schemes.
Author Lin, Zhifei
Liu, Licheng
Wang, Feng
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  email: celcliu@gdut.edu.cn
  organization: School of Information Engineering, Guangdong University of Technology,Guangzhou,China,510006
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Snippet This paper considers an energy harvesting (EH) based multiuser mobile edge computing (MEC) system, where each user utilizes the harvested energy from renewable...
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StartPage 312
SubjectTerms Benchmark testing
computation offloading
Convex functions
convex optimization
Energy harvesting
energy harvesting (EH)
Mobile edge computing (MEC)
Performance gain
Renewable energy sources
Resource management
Task analysis
Title Computation Rate Maximization for Multiuser Mobile Edge Computing Systems With Dynamic Energy Arrivals
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